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Statistical Methods for Assessing the Explained Variation of a Health Outcome by a Mixture of Exposures

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  • Hua Yun Chen

    (Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA)

  • Hesen Li

    (Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA)

  • Maria Argos

    (Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA)

  • Victoria W. Persky

    (Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA)

  • Mary E. Turyk

    (Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA)

Abstract

Exposures to environmental pollutants are often composed of mixtures of chemicals that can be highly correlated because of similar sources and/or chemical structures. The effect of an individual chemical on a health outcome can be weak and difficult to detect because of the relatively low level of exposures to many environmental pollutants. To tackle the challenging problem of assessing the health risk of exposure to a mixture of environmental pollutants, we propose a statistical approach to assessing the proportion of the variation of an outcome explained by a mixture of pollutants. The proposed approach avoids the difficult task of identifying specific pollutants that are responsible for the effects and may also be used to assess interactions among exposures. Extensive simulation results demonstrate that the proposed approach has very good performance. Application of the proposed approach is illustrated by investigating the main and interaction effects of the chemical pollutants on systolic and diastolic blood pressure in participants from the National Health and Nutrition Examination Survey.

Suggested Citation

  • Hua Yun Chen & Hesen Li & Maria Argos & Victoria W. Persky & Mary E. Turyk, 2022. "Statistical Methods for Assessing the Explained Variation of a Health Outcome by a Mixture of Exposures," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:5:p:2693-:d:758602
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    References listed on IDEAS

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    Cited by:

    1. Bonnie R. Joubert & Marianthi-Anna Kioumourtzoglou & Toccara Chamberlain & Hua Yun Chen & Chris Gennings & Mary E. Turyk & Marie Lynn Miranda & Thomas F. Webster & Katherine B. Ensor & David B. Dunson, 2022. "Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods," IJERPH, MDPI, vol. 19(3), pages 1-24, January.

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